City of Melbourne parking
Exploring 4M+ parking sensor records to understand demand and inform better use of city space.
Concept illustration for the project. This is not a chart of the underlying dataset.
The question
How can parking sensor data help explain occupancy patterns and support parking management decisions?
An industry capstone exploring City of Melbourne parking sensor data in a cross-functional team.
My contribution
Built an analytics pipeline to process parking sensor records, explored occupancy patterns, and developed occupancy prediction models.
The approach
- Processed sensor records into an analysis-ready dataset.
- Visualised occupancy by zone, time of day, and day of week in Python.
- Developed prediction models and presented findings and recommendations to project leaders.
What the work supported
The analysis examined how occupancy varied across locations and time periods, providing a basis for discussing parking management options.
Scope and limitations
The project developed recommendations. It did not establish that congestion fell or that the recommendations were implemented.